Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The struc...Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.展开更多
Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full networ...Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.展开更多
Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree...Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.展开更多
Bearing-based hunting protocols commonly adopt a leaderless consensus method,which requests an entire state of the target for each agent and ignores the necessity of collision avoidance.We investigate a hunting proble...Bearing-based hunting protocols commonly adopt a leaderless consensus method,which requests an entire state of the target for each agent and ignores the necessity of collision avoidance.We investigate a hunting problem of multi-quadrotor systems with hybrid bearing protocols,where the quadrotor systems are divided into master and slave groups for reducing the onboard loads and collision avoidance.The masters obtain the entire state of the target,whose hybrid protocols are based on the displacement and bearing constraints to maintain formation and to avoid the collision in the hunting process.However,the slaves’protocols merely depend on the part state of the masters to reduce loads of data transmission.We also investigate the feasibility of receiving the bearing state from machine vision.The simulation results are given to illustrate the effectiveness of the proposed hybrid bearing protocols.展开更多
Making use of Microsoft Visual Studio.NET platform,hierarchical network planning is realized in working procedure time-optimization of the construction by TBM,and hierarchical network graph of the construction by TBM ...Making use of Microsoft Visual Studio.NET platform,hierarchical network planning is realized in working procedure time-optimization of the construction by TBM,and hierarchical network graph of the construction by TBM is drawn based on browser.Then the theory of system realization is discussed,six components of system that can be reused are explained emphatically.The realization of hierarchical network panning in Internet provides available guarantee for controlling rate of progress in large-scale or middle-sized projects.展开更多
Quantum networks with multiple sources always face performance challenges due to the vulnerability of quantum systems. Thus, it is highly desirable to have the capability to continuously monitor and determine the exac...Quantum networks with multiple sources always face performance challenges due to the vulnerability of quantum systems. Thus, it is highly desirable to have the capability to continuously monitor and determine the exact number of quantum sources versus classical sources present within the network. Hierarchical network nonlocality can reveal the relationship between network nonlocality and the number of quantum sources within the network, thus becoming a way to address the aforementioned issue. However, up to now, precise hierarchical network nonlocality inequalities have only been established for star networks, and cannot be obtained for other non-star structured networks [Phys. Rev. Lett. 128 010403(2022) and Phys. Rev. A 110 022617(2024)]. In this paper, we establish more refined criteria for hierarchical network nonlocality inequalities in arbitrary network structures. Violating such inequalities can reveal the exact number of quantum sources in the network. These results enhance the understanding of quantum source distribution in complex network topologies.展开更多
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.展开更多
Demineralized bone matrix(DBM)is one of the standard biomaterials used to fill surgical bone defects in general orthopedic procedures.However,current DBM products come in the form of powder or viscous solutions that f...Demineralized bone matrix(DBM)is one of the standard biomaterials used to fill surgical bone defects in general orthopedic procedures.However,current DBM products come in the form of powder or viscous solutions that fail to mimic the natural hierarchical structure of bone while also using large amounts of valuable material.To overcome this,compact fibrous DBM/polymer(fDBM)composites were prepared via electrospinning.Then,by exploiting the catalytic decomposition of hydrogen peroxide,oxygen pockets are formed in the scaffold imparting it with a hierarchical porous structure similar to bone(Op-fDBM).These pockets created by bubbles of oxygen help give the scaffold a mechanically stable shape while the incorporated DBM supports cell adhesion and growth.In vivo evaluations reveal that fDBM increased bone volume by 41.7%while Op-fDBM increased bone volume by 68.6%.Significant increases in regenerated bone volume with the use of minimal amounts of DBM in fiber form go to show the great potential of this work in the field of bone regeneration.展开更多
The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns.Traditional stan...The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns.Traditional stance detection methods often struggle with understanding limited context and have insufficient generalization across diverse sentiments and semantic structures.This paper focuses on effectively mining and utilizing sentimentsemantics knowledge for stance knowledge transfer and proposes a sentiment-aware hierarchical attention network(SentiHAN)for cross-target stance detection.SentiHAN introduces an improved hierarchical attention network designed to maximize the use of high-level representations of targets and texts at various fine-grain levels.This model integrates phrase-level combinatorial sentiment knowledge to effectively bridge the knowledge gap between known and unknown targets.By doing so,it enables a comprehensive understanding of stance representations for unknown targets across different sentiments and semantic structures.The model’s ability to leverage sentimentsemantics knowledge enhances its performance in detecting stances that may not be directly observable from the immediate context.Extensive experimental results indicate that SentiHAN significantly outperforms existing benchmark methods in terms of both accuracy and robustness.Moreover,the paper employs ablation studies and visualization techniques to explore the intricate relationship between sentiment and stance.These analyses further confirm the effectiveness of sentence-level combinatorial sentiment knowledge in improving stance detection capabilities.展开更多
Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks （HWSNs）, espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink...Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks （HWSNs）, espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are con- sidered as players. By applying a double-pricing scheme in the definition of OHSs＇ utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effec- tiveness of the proposed algorithm.展开更多
For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P...For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.展开更多
To solve the load balancing problem in a triplet-based hierarchical interconnection network（THIN） system, a dynamic load balancing （DLB）algorithm--THINDLBA, which adopts multicast tree （MT）technology to improve ...To solve the load balancing problem in a triplet-based hierarchical interconnection network（THIN） system, a dynamic load balancing （DLB）algorithm--THINDLBA, which adopts multicast tree （MT）technology to improve the efficiency of interchanging load information, is presented. To support the algorithm, a complete set of DLB messages and a schema of maintaining DLB information in each processing node are designed. The load migration request messages from the heavily loaded node （HLN）are spread along an MT whose root is the HLN. And the lightly loaded nodes（LLNs） covered by the MT are the candidate destinations of load migration; the load information interchanged between the LLNs and the HLN can be transmitted along the MT. So the HLN can migrate excess loads out as many as possible during a one time execution of the THINDLBA, and its load state can be improved as quickly as possible. To avoid wrongly transmitted or redundant DLB messages due to MT overlapping, the MT construction is restricted in the design of the THINDLBA. Through experiments, the effectiveness of four DLB algorithms are compared, and the results show that the THINDLBA can effectively decrease the time costs of THIN systems in dealing with large scale computeintensive tasks more than others.展开更多
It is shown that the cortical brain network of the macaque displays a hierarchically clustered organizationand the neuron network shows small-world properties.Now the two factors will be considered in our model and th...It is shown that the cortical brain network of the macaque displays a hierarchically clustered organizationand the neuron network shows small-world properties.Now the two factors will be considered in our model and thedynamical behavior of the model will be studied.We study the characters of the model and find that the distribution ofavalanche size of the model follows power-law behavior.展开更多
In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined...In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.展开更多
In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile sta...In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow.Then, according to the mobility classification,network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.展开更多
To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets ...To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts: groups of subnets based on well trained Autoassociative Neural Networks(AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method,the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification(EDAC) is adopted. Soft sensor using AHNN based on EDAC(EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid(PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.展开更多
Entity matching is a fundamental problem of data integration.It groups records according to underlying real-world entities.There is a growing trend of entity matching via deep learning techniques.We design mixed hiera...Entity matching is a fundamental problem of data integration.It groups records according to underlying real-world entities.There is a growing trend of entity matching via deep learning techniques.We design mixed hierarchical deep neural networks(MHN)for entity matching,exploiting semantics from different abstract levels in the record internal hierarchy.A family of attention mechanisms is utilized in different periods of entity matching.Self-attention focuses on internal dependency,inter-attention targets at alignments,and multi-perspective weight attention is devoted to importance discrimination.Especially,hybrid soft token alignment is proposed to address corrupted data.Attribute order is for the first time considered in deep entity matching.Then,to reduce utilization of labeled training data,we propose an adversarial domain adaption approach(DA-MHN)to transfer matching knowledge between different entity matching tasks by maximizing classifier discrepancy.Finally,we conduct comprehensive experimental evaluations on 10 datasets(seven for MHN and three for DA-MHN),which illustrate our two proposed approaches1 superiorities.MHN apparently outperforms previous studies in accuracy,and also each component of MHN is tested.DA-MHN greatly surpasses existing studies in transferability.展开更多
Human settlements are embedded in traffic networks with hierarchical structures. In order to understand the spreading mechanism of infectious diseases and deploy control measures, the susceptible-infected-removed spre...Human settlements are embedded in traffic networks with hierarchical structures. In order to understand the spreading mechanism of infectious diseases and deploy control measures, the susceptible-infected-removed spreading process is studied with agents moving globally on the hierarchical geographic network, taking into account agents’ preference for node layers and memory of initial nodes. We investigate the spreading behavior in the case of global infection under different scenarios, including different directions of human flow, different locations of infection source, and different moving behaviors of agents between layers. Based on the above-mentioned analysis, we propose screening strategies based on layer rank and moving distance, and compare their effects on delaying epidemic spreading. We find that in the case of global infection,infection spreads faster in high layers than in low layers, and early infection in high layers and moving to high layers both accelerate epidemic spreading. Travels of high-layer and low-layer residents have different effects on accelerating epidemic spreading, and moving between high and low layers increases the peak value of new infected cases more than moving in the same layer or between adjacent layers. Infection in intermediate nodes enhances the effects of moving of low-layer residents more than the moving of high-layer residents on accelerating epidemic spreading. For screening measures, improving the success rate is more effective on delaying epidemic spreading than expanding the screening range. With the same number of moves screened, screening moves into or out of high-layer nodes combined with screening moves between subnetworks has better results than only screening moves into or out of high-layer nodes, and screening long-distance moves has the worst results when the screening range is small, but it achieves the best results in reducing the peak value of new infected cases when the screening range is large enough. This study probes into the spreading process and control measures under different scenarios on the hierarchical geographical network, and is of great significance for epidemic control in the real world.展开更多
SMS spam poses a significant challenge to maintaining user privacy and security.Recently,spammers have employed fraudulent writing styles to bypass spam detection systems.This paper introduces a novel two-level detect...SMS spam poses a significant challenge to maintaining user privacy and security.Recently,spammers have employed fraudulent writing styles to bypass spam detection systems.This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam.The system comprises five steps,beginning with the preprocessing of SMS data.RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis.Feature extraction is performed using a Convolutional Neural Network(CNN)for word-level analysis and a Bidirectional Long Short-Term Memory(BiLSTM)for sentence-level analysis.The two-level feature extraction enables a complete understanding of individual words and sentence structure.The novel part of the proposed approach is the Hierarchical Attention Network(HAN),which fuses and selects features at two levels through an attention mechanism.The HAN can deal with words and sentences to focus on the most pertinent aspects of messages for spam detection.This network is productive in capturing meaningful features,considering both word-level and sentence-level semantics.In the classification step,the model classifies the messages into spam and ham.This hybrid deep learning method improve the feature representation,and enhancing the model’s spam detection capabilities.By significantly reducing the incidence of SMS spam,our model contributes to a safer mobile communication environment,protecting users against potential phishing attacks and scams,and aiding in compliance with privacy and security regulations.This model’s performance was evaluated using the SMS Spam Collection Dataset from the UCI Machine Learning Repository.Cross-validation is employed to consider the dataset’s imbalanced nature,ensuring a reliable evaluation.The proposed model achieved a good accuracy of 99.48%,underscoring its efficiency in identifying SMS spam.展开更多
The secondary cell wall(SCW)is essential for plant growth and development in vascular plants,and its biosynthesis is mainly controlled by a complex hierarchical regulatory network involving multiple transcription fact...The secondary cell wall(SCW)is essential for plant growth and development in vascular plants,and its biosynthesis is mainly controlled by a complex hierarchical regulatory network involving multiple transcription factors(TFs)at the transcription level.However,TFs that specifically regulate secondary xylem have not been widely reported.In this study,we described a poplar KNOTTED1-like homeobox(KNOX)TF PtoKNAT3a1,which was mainly expressed in the expanding xylem cells of stems.PtoKNAT3a1 overexpression caused fiber SCW thickening and increased all measured SCW compositions by upregulating the expression of SCW-biosynthetic genes and-associated TFs,but had no effect on the vessels of SCW.The opposite phenotype was observed in the PtoKNAT3a1-knockout lines.Hence,we further demonstrated that Pto-KNAT3a1 could physically interact with the NAC master switches PtoWND2A/3A to enhance the expression of downstream MYB TFs and SCW biosynthetic genes(including PtoMYB20,PtoMYB21,PtoMYB90,PtoCoMT2,PtoGT43B and PtoCesA8).Meanwhile,the studies also demonstrate that the KNAT3 has functional differentiation in xylem development.Taken together,these data suggest that the KNAT3a1-WND2A/3A module positively regulates fiber development of the secondary xylem in poplar via the WND2A/3A-mediated hierarchical regulatory network,and supplies useful information for fiber SCW formation.The research not only deepens the understanding of the hierarchical regulatory network affecting SCW formation but also supplies genetic resources and molecular targets for plant fiber utilization.展开更多
基金supported in part by the National Natural Science Foundation of China(62225306,U2141235,52188102,and 62003145)the National Key Research and Development Program of China(2022ZD0119601)+1 种基金Guangdong Basic and Applied Research Foundation(2022B1515120069)the Science and Technology Project of State Grid Corporation of China(5100-202199557A-0-5-ZN).
文摘Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12271394 and 12071336)the Key Research and Development Program of Shanxi Province(Grant No.202102010101004)。
文摘Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.
基金Project supported by the Natural Science Foundation of ZhejiangProvince, China (No. Y105697)the Ningbo Natural ScienceFoundation,China (No. 2005A610004)
文摘Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61673217 and 61673214)the National Defense Basic Scientific Research Program of China(Grant No.JCKY2019606D001)the China Scholarship Council.
文摘Bearing-based hunting protocols commonly adopt a leaderless consensus method,which requests an entire state of the target for each agent and ignores the necessity of collision avoidance.We investigate a hunting problem of multi-quadrotor systems with hybrid bearing protocols,where the quadrotor systems are divided into master and slave groups for reducing the onboard loads and collision avoidance.The masters obtain the entire state of the target,whose hybrid protocols are based on the displacement and bearing constraints to maintain formation and to avoid the collision in the hunting process.However,the slaves’protocols merely depend on the part state of the masters to reduce loads of data transmission.We also investigate the feasibility of receiving the bearing state from machine vision.The simulation results are given to illustrate the effectiveness of the proposed hybrid bearing protocols.
文摘Making use of Microsoft Visual Studio.NET platform,hierarchical network planning is realized in working procedure time-optimization of the construction by TBM,and hierarchical network graph of the construction by TBM is drawn based on browser.Then the theory of system realization is discussed,six components of system that can be reused are explained emphatically.The realization of hierarchical network panning in Internet provides available guarantee for controlling rate of progress in large-scale or middle-sized projects.
基金Project supported by the National Natural Science Foundation of China (Grant No. 12271394)。
文摘Quantum networks with multiple sources always face performance challenges due to the vulnerability of quantum systems. Thus, it is highly desirable to have the capability to continuously monitor and determine the exact number of quantum sources versus classical sources present within the network. Hierarchical network nonlocality can reveal the relationship between network nonlocality and the number of quantum sources within the network, thus becoming a way to address the aforementioned issue. However, up to now, precise hierarchical network nonlocality inequalities have only been established for star networks, and cannot be obtained for other non-star structured networks [Phys. Rev. Lett. 128 010403(2022) and Phys. Rev. A 110 022617(2024)]. In this paper, we establish more refined criteria for hierarchical network nonlocality inequalities in arbitrary network structures. Violating such inequalities can reveal the exact number of quantum sources in the network. These results enhance the understanding of quantum source distribution in complex network topologies.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
文摘In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)(Project No.2016R1C1B2008296)It was also partially supported by the Na-tional Research Foundation of Korea(NRF)grant funded by the Korean Ministry of Education(Nos.NRF-2021R1I1A1A01047751 and RS-2023-00240020).
文摘Demineralized bone matrix(DBM)is one of the standard biomaterials used to fill surgical bone defects in general orthopedic procedures.However,current DBM products come in the form of powder or viscous solutions that fail to mimic the natural hierarchical structure of bone while also using large amounts of valuable material.To overcome this,compact fibrous DBM/polymer(fDBM)composites were prepared via electrospinning.Then,by exploiting the catalytic decomposition of hydrogen peroxide,oxygen pockets are formed in the scaffold imparting it with a hierarchical porous structure similar to bone(Op-fDBM).These pockets created by bubbles of oxygen help give the scaffold a mechanically stable shape while the incorporated DBM supports cell adhesion and growth.In vivo evaluations reveal that fDBM increased bone volume by 41.7%while Op-fDBM increased bone volume by 68.6%.Significant increases in regenerated bone volume with the use of minimal amounts of DBM in fiber form go to show the great potential of this work in the field of bone regeneration.
基金supported by the National Social Science Fund of China(20BXW101)。
文摘The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns.Traditional stance detection methods often struggle with understanding limited context and have insufficient generalization across diverse sentiments and semantic structures.This paper focuses on effectively mining and utilizing sentimentsemantics knowledge for stance knowledge transfer and proposes a sentiment-aware hierarchical attention network(SentiHAN)for cross-target stance detection.SentiHAN introduces an improved hierarchical attention network designed to maximize the use of high-level representations of targets and texts at various fine-grain levels.This model integrates phrase-level combinatorial sentiment knowledge to effectively bridge the knowledge gap between known and unknown targets.By doing so,it enables a comprehensive understanding of stance representations for unknown targets across different sentiments and semantic structures.The model’s ability to leverage sentimentsemantics knowledge enhances its performance in detecting stances that may not be directly observable from the immediate context.Extensive experimental results indicate that SentiHAN significantly outperforms existing benchmark methods in terms of both accuracy and robustness.Moreover,the paper employs ablation studies and visualization techniques to explore the intricate relationship between sentiment and stance.These analyses further confirm the effectiveness of sentence-level combinatorial sentiment knowledge in improving stance detection capabilities.
基金supported by the National Natural Science Foundation of China (7070102571071105)+2 种基金the Program for New Century Excellent Talents in Universities of China (NCET-08-0396)the National Science Fund for Distinguished Young Scholars of China (70925005)the Program for Changjiang Scholars and Innovative Research Team in University (IRT/028)
文摘Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks （HWSNs）, espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are con- sidered as players. By applying a double-pricing scheme in the definition of OHSs＇ utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effec- tiveness of the proposed algorithm.
文摘For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.
基金The National Natural Science Foundation of China(No.69973007).
文摘To solve the load balancing problem in a triplet-based hierarchical interconnection network（THIN） system, a dynamic load balancing （DLB）algorithm--THINDLBA, which adopts multicast tree （MT）technology to improve the efficiency of interchanging load information, is presented. To support the algorithm, a complete set of DLB messages and a schema of maintaining DLB information in each processing node are designed. The load migration request messages from the heavily loaded node （HLN）are spread along an MT whose root is the HLN. And the lightly loaded nodes（LLNs） covered by the MT are the candidate destinations of load migration; the load information interchanged between the LLNs and the HLN can be transmitted along the MT. So the HLN can migrate excess loads out as many as possible during a one time execution of the THINDLBA, and its load state can be improved as quickly as possible. To avoid wrongly transmitted or redundant DLB messages due to MT overlapping, the MT construction is restricted in the design of the THINDLBA. Through experiments, the effectiveness of four DLB algorithms are compared, and the results show that the THINDLBA can effectively decrease the time costs of THIN systems in dealing with large scale computeintensive tasks more than others.
基金supported by National Natural Science Foundation of China under Grant No.10675060
文摘It is shown that the cortical brain network of the macaque displays a hierarchically clustered organizationand the neuron network shows small-world properties.Now the two factors will be considered in our model and thedynamical behavior of the model will be studied.We study the characters of the model and find that the distribution ofavalanche size of the model follows power-law behavior.
基金supported by the Ministry of Education,Science,Sports and Culture,Grant-in-Aid for Scientific Research under Grant No.22240021the Grant-in-Aid for Challenging Exploratory Research under Grant No.21650030
文摘In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.
基金supported by Natural Science Foundation of China(61372125)973 project(2013CB329104)+1 种基金the National High-Tech R&D Program(863 Program 2015AA01A705)the open research fund of National Mobile Communications Research Laboratory,Southeast University(2013D01)
文摘In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow.Then, according to the mobility classification,network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.
基金Supported by the National Natural Science Foundation of China(61074153)
文摘To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network(AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts: groups of subnets based on well trained Autoassociative Neural Networks(AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method,the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification(EDAC) is adopted. Soft sensor using AHNN based on EDAC(EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid(PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.
基金the National Natural Science Foundation of China under Grant Nos.62002262,61672142,61602103,62072086 and 62072084the National Key Research and Development Project of China under Grant No.2018YFB1003404.
文摘Entity matching is a fundamental problem of data integration.It groups records according to underlying real-world entities.There is a growing trend of entity matching via deep learning techniques.We design mixed hierarchical deep neural networks(MHN)for entity matching,exploiting semantics from different abstract levels in the record internal hierarchy.A family of attention mechanisms is utilized in different periods of entity matching.Self-attention focuses on internal dependency,inter-attention targets at alignments,and multi-perspective weight attention is devoted to importance discrimination.Especially,hybrid soft token alignment is proposed to address corrupted data.Attribute order is for the first time considered in deep entity matching.Then,to reduce utilization of labeled training data,we propose an adversarial domain adaption approach(DA-MHN)to transfer matching knowledge between different entity matching tasks by maximizing classifier discrepancy.Finally,we conduct comprehensive experimental evaluations on 10 datasets(seven for MHN and three for DA-MHN),which illustrate our two proposed approaches1 superiorities.MHN apparently outperforms previous studies in accuracy,and also each component of MHN is tested.DA-MHN greatly surpasses existing studies in transferability.
基金Project supported by the National Key R&D Program of China(Grant No.2018YFF0301005)the National Natural Science Foundation of China(Grant Nos.71673161 and 71790613)the Collaborative Innovation Center of Public Safety,China
文摘Human settlements are embedded in traffic networks with hierarchical structures. In order to understand the spreading mechanism of infectious diseases and deploy control measures, the susceptible-infected-removed spreading process is studied with agents moving globally on the hierarchical geographic network, taking into account agents’ preference for node layers and memory of initial nodes. We investigate the spreading behavior in the case of global infection under different scenarios, including different directions of human flow, different locations of infection source, and different moving behaviors of agents between layers. Based on the above-mentioned analysis, we propose screening strategies based on layer rank and moving distance, and compare their effects on delaying epidemic spreading. We find that in the case of global infection,infection spreads faster in high layers than in low layers, and early infection in high layers and moving to high layers both accelerate epidemic spreading. Travels of high-layer and low-layer residents have different effects on accelerating epidemic spreading, and moving between high and low layers increases the peak value of new infected cases more than moving in the same layer or between adjacent layers. Infection in intermediate nodes enhances the effects of moving of low-layer residents more than the moving of high-layer residents on accelerating epidemic spreading. For screening measures, improving the success rate is more effective on delaying epidemic spreading than expanding the screening range. With the same number of moves screened, screening moves into or out of high-layer nodes combined with screening moves between subnetworks has better results than only screening moves into or out of high-layer nodes, and screening long-distance moves has the worst results when the screening range is small, but it achieves the best results in reducing the peak value of new infected cases when the screening range is large enough. This study probes into the spreading process and control measures under different scenarios on the hierarchical geographical network, and is of great significance for epidemic control in the real world.
文摘SMS spam poses a significant challenge to maintaining user privacy and security.Recently,spammers have employed fraudulent writing styles to bypass spam detection systems.This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam.The system comprises five steps,beginning with the preprocessing of SMS data.RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis.Feature extraction is performed using a Convolutional Neural Network(CNN)for word-level analysis and a Bidirectional Long Short-Term Memory(BiLSTM)for sentence-level analysis.The two-level feature extraction enables a complete understanding of individual words and sentence structure.The novel part of the proposed approach is the Hierarchical Attention Network(HAN),which fuses and selects features at two levels through an attention mechanism.The HAN can deal with words and sentences to focus on the most pertinent aspects of messages for spam detection.This network is productive in capturing meaningful features,considering both word-level and sentence-level semantics.In the classification step,the model classifies the messages into spam and ham.This hybrid deep learning method improve the feature representation,and enhancing the model’s spam detection capabilities.By significantly reducing the incidence of SMS spam,our model contributes to a safer mobile communication environment,protecting users against potential phishing attacks and scams,and aiding in compliance with privacy and security regulations.This model’s performance was evaluated using the SMS Spam Collection Dataset from the UCI Machine Learning Repository.Cross-validation is employed to consider the dataset’s imbalanced nature,ensuring a reliable evaluation.The proposed model achieved a good accuracy of 99.48%,underscoring its efficiency in identifying SMS spam.
基金supported by grants from the Biological Breeding-National Science and Technology Major Project(Grant No.2023ZD0406803)the National Key Research and Development Program(Grant No.2021YFD2200204)+2 种基金the National Science Foundation of China(Grant No.32071791 and 32271835)the Chongqing Youth Top Talent Program(Grant No.CQYC201905028)Fundamental Research Funds for the Central Universities(Grant No.XDJK2020B036).
文摘The secondary cell wall(SCW)is essential for plant growth and development in vascular plants,and its biosynthesis is mainly controlled by a complex hierarchical regulatory network involving multiple transcription factors(TFs)at the transcription level.However,TFs that specifically regulate secondary xylem have not been widely reported.In this study,we described a poplar KNOTTED1-like homeobox(KNOX)TF PtoKNAT3a1,which was mainly expressed in the expanding xylem cells of stems.PtoKNAT3a1 overexpression caused fiber SCW thickening and increased all measured SCW compositions by upregulating the expression of SCW-biosynthetic genes and-associated TFs,but had no effect on the vessels of SCW.The opposite phenotype was observed in the PtoKNAT3a1-knockout lines.Hence,we further demonstrated that Pto-KNAT3a1 could physically interact with the NAC master switches PtoWND2A/3A to enhance the expression of downstream MYB TFs and SCW biosynthetic genes(including PtoMYB20,PtoMYB21,PtoMYB90,PtoCoMT2,PtoGT43B and PtoCesA8).Meanwhile,the studies also demonstrate that the KNAT3 has functional differentiation in xylem development.Taken together,these data suggest that the KNAT3a1-WND2A/3A module positively regulates fiber development of the secondary xylem in poplar via the WND2A/3A-mediated hierarchical regulatory network,and supplies useful information for fiber SCW formation.The research not only deepens the understanding of the hierarchical regulatory network affecting SCW formation but also supplies genetic resources and molecular targets for plant fiber utilization.
